• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.75-87
• /
• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• KDI Journal of Economic Policy
• /
• v.37 no.4
• /
• pp.75-88
• /
• 2015

This study presents a real-time impact evaluation of a human capacity-building health project in Laos, financed by a Korean aid agency and executed jointly by Laotian and Korean higher educational agencies. The project aims to improve the health status of Laotians by enhancing practicing doctors' clinical performance capacity, to be attained by advancing academic achievement at the University of Health Sciences (UHS) in Laos. Therefore, this real-time impact evaluation adopted the difference-in-differences regression analysis method, showing that the project improved the academic achievement of the UHS students who were taught by the project fellowship awardees more, compared to the UHS students who were taught by non-fellowship faculty members. It remains to be evaluated whether these UHS students taught by the project fellowship recipients would also perform better clinically in public hospitals in the future.

• Journal of information and communication convergence engineering
• /
• v.9 no.4
• /
• pp.353-357
• /
• 2011

U.S. Obama government is submit a motion to consider cyber attacks on State as a war. 7.7DDoS attack in Korea in 2009 and 3.4 DDoS attacks 2011, the country can be considered about cyber attacks. China hackers access a third country, bypassing South Korea IP by hacking the e-commerce sites with fake account, that incident was damaging finance. In this paper, for WiBro service, DDoS attacks, hackers, security incidents and vulnerabilities to the analysis. From hacker's attack, WiBro service's prognostic relevance by analyzing symptoms and attacks, in real time, Divide Red, Orange, Yellow, Green belonging to the risk rating. For hackers to create a blacklist, to defend against attacks in real-time air-conditioning system is the study of security. WiBro networks for incident tracking and detection after the packets through the national incident response should contribute to the development of technology.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.5
• /
• pp.1239-1244
• /
• 2013

In the field of clinical medicine and research, the analysis of such as protein and DNA at the molecular level and even at the cell level are necessary for disease diagnosis and treatment. In many cases, a real time image of samples is needed for the accurate analysis and manipulation of samples since experimental samples are degenerated with time. In this research, a three-dimensional fluorescence microscope device was developed for taking images of protein and DNA inside a single cell and the server-client based image analysis system was made for an integrated management of the real-time images taken from the microscope device. The system consists of a fluorescent measurement device, the associated software and a client program on smartphone. The developed system allows doctors or experimental managers to receive and look at the real-time experimental images taken from the samples of patients anywhere in the emergency, to analyze results and to instantly diagnose the disease and to transfer the results to the patients. As a result, the system is able to be utilized in the implementation of ubiquitous health as well.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.157-166
• /
• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.6
• /
• pp.1753-1765
• /
• 2000

As the areas of computer application are expanded, real-time application environments that must process as many transactions as possible within their deadlines, such as a stock transaction systems, ATM switching systems etc, have been increased recently. The reason why the conventional database systems can't process soft real-time applications is the lack of prediction and poor performance on processing transaction's deadline. If transactions want to access data stored at the secondary storage, they can not satisfy requirements of real-time applications because of the disk delay time. This paper designs a main-memory database prototype systems to be suitable to real-time applications and then this system can produce rapid results without disk i/o as all of the information are loaded in main memory database. In thesis proposed the improved techniques with respect to logging, checkpointing, and recovering in our environment. In order to improve the performance of the system, a) the frequency of log analysis and redo processing is reduced by the proposed redo technique at system failure, b) database consistency is maintained by improved fuzzy checkpointing. The performance model is proposed which consists of two parts. The first part evaluates log processing time for recovery and compares with other research activities. The second part examines checkpointing behavior.

• Journal of Food Hygiene and Safety
• /
• v.34 no.6
• /
• pp.557-561
• /
• 2019

This study investigated the efficacy of DNA extraction methods for real-time PCR detection of foodborne pathogenic bacteria in livestock manure composts. Livestock manure composts were inoculated with Escherichia coli O157:H7, Salmonella Typhimurium, Listeria monocytogenes, Bacillus cereus and incubated in enrichment broth. For DNA extraction, enriched samples were treated following boiling method, by chloroform, C₁₈ powder, and proteinase K. As a result, 4 species of bacteria were detected by real-time PCR when subjected to boiling for 30 min and treated with proteinase K. These results suggest that detection of foodborne pathogens by real-time PCR from livestock manure composts could be applicable using effective DNA extraction methodology such as the boiling method or proteinase K.

• KIEE International Transactions on Power Engineering
• /
• v.11A no.4
• /
• pp.33-38
• /
• 2001

The KEPS(KEPCO＇s Enhanced Power system Simulator) Real Time Digital Simulator(RTDS) is the largest real time power system simulator ever built. A power system which includes 320 (3-phase) buses and 90 generators has been modeled and run in real time. Since such large-scale systems were involved, it was not practical to validate them using non-real time electro-magnetic transient programs such as EMTDC™ or EMTP. Instead, the results of the real time electromagnetic transient simulation were validated by comparing to transient stability simulations run using PTI's PSS/E™ program. The comparison of results from the two programs is very good in almost all cases. However, as expected, some differences did exist and were investigated. The differences in the results were primarily traced to the fact that the electromagnetic transient solution algorithm provides more detail solutions and therefore greater accuracy than the transient stability algorithm. After finding very good comparison of results between RTDS Simulator and PSS/E, and after investigating the discrepancies found, KEPCO gained the necessary confidence to use the large-scale real time simulator to analyze and develop their power system.

• Journal of Food Hygiene and Safety
• /
• v.29 no.1
• /
• pp.31-39
• /
• 2014

Norovirus causes acute gastroenteritis in all age groups and its food poisoning outbreaks are rapidly increasing in Korea. Reverse transcription-polymerase chain reaction (RT-PCR) is most widely used for the rapid detection of foodborne viruses due to high sensitivity. However, the false positive results of RT-PCR obtained against already inactivated viruses could be a serious drawbacks in food safety area. In this study, we investigated a method to yield true positive RT-PCR results only with alive viruses. To decompose the RNA genes from dead viruses, the enzymatic treatments composed of proteinse K and Ribonuclease A were applied to the sanitized and inactivated virus particles. Another aim of this study was to quantify the efficiencies of several major sanitizing treatments using real-time RT-PCR. Feline calicivirus (FCV) that belongs to the same Caliciviridae family with norovirus was used as a surrogate model for norovirus. The initial level of virus in control suspension was approximately $10^4$ PFU/mL. Most of inactivated viruses treated with the enzymatic treatment for 30 min at $37^{\circ}C$ were not detected in RT-PCR, Quantification results to verify the inactivation efficiencies of sanitizing treatments using real-time RT-PCR showed no false positive in most cases. We could successfully develope a numerical quantification process for the inactivated viruses after major sanitizing treatments using real-time RT-PCR. The results obtained in this study could provide a novel basis of rapid virus quantification in food safety area.

• Child Health Nursing Research
• /
• v.30 no.2
• /
• pp.142-153
• /
• 2024

Purpose: This study aimed to describe the process of developing a validated pediatric nursing simulation scenario template using the real-time Delphi method. Methods: A panel of 13 pediatric nursing experts participated in a real-time Delphi survey conducted over two rounds. Initially, 83 items were included in the questionnaire focusing on the structure and content of the simulation scenario template. Data analysis involved calculating the content validity ratio (CVR) and the coefficient of variation to assess item validity and stability. Results: Through iterative rounds of the Delphi survey, a consensus was reached among the experts, resulting in the development of a pediatric nursing simulation scenario template comprising 41 items across nine parts. The CVR values ranged from 0.85 to 1.0, indicating a high consensus among experts regarding the inclusion of all items in the template. Conclusion: This study presents a novel approach for developing a pediatric nursing simulation scenario template using real-time Delphi methods. The real-time Delphi method facilitated the development of a comprehensive and scientifically grounded pediatric nursing simulation scenario template. Our template aligns with the International Nursing Association for Clinical Simulation and Learning standards, and provides valuable guidance for educators in designing effective simulation scenarios, contributing to enhanced learning outcomes and better preparation for pediatric clinical practice. However, consideration of cultural and contextual adaptations is necessary, and further research should explore alternative consensus criteria.